PipelineDP4j: update readmes

Change-Id: I349b873f1adf9a0e548ebb1e3ed7e0b2a7c30b58
GitOrigin-RevId: b164dd3b302b61013f257e821d14277e25320e57

Author: Differential Privacy Team

cc/algorithms/approx-bounds-provider.h

@@ -169,6 +169,7 @@ class ApproxBoundsProvider final : public BoundsProvider<T> {
 
     RETURN_IF_ERROR(ValidateIsInExclusiveInterval(options.success_probability,
                                                   0, 1, "Success probability"));
+
     // Using `new` to access non-public constructor.
     return absl::WrapUnique(new ApproxBoundsProvider<T>(
         options.epsilon, options.num_bins, options.scale, options.base,

examples/pipelinedp4j/README.md

@@ -102,7 +102,7 @@ Here are the steps to build and run the example assuming you are in the
     ```
 
 1.  Run the program (if you want to run Spark example, change `beam` to `spark`,
-    `BeamExample` to `SparkExample` or `SparkDatasetExample` and
+    `BeamExample` to `SparkExample` or `SparkDataFrameExample` and
     `--outputFilePath=output.txt` to `--outputFolder=output`):
 
     ```shell
@@ -124,12 +124,6 @@ utilizes a
 from the Maven repository, eliminating the need for the library source files and
 the Bazel files (`WORKSPACE.bazel`, `.bazelversion` and `BUILD.bazel`).
 
-NOTE: Currently the version of examples at HEAD does not build with Maven
-because the examples were migrated to the new version of public API which has
-not been uploaded to the Maven repository yet. It will be uploaded in a few
-days. For now, use the version of examples from commit that preceded the commit
-when this note was created (use git blame).
-
 To proceed, ensure Maven is installed on your system. If you're using Linux or
 MacOS, you can install it by running `sudo apt-get install maven` or `brew
 install maven`, respectively. While any Maven version should work, refer to
@@ -147,7 +141,7 @@ Once Maven is installed, navigate to the directory of a backend you want to use:
 *   `examples/pipelinedp4j/spark` for Spark
 
 Then execute the following command with updated `inputFilePath` (if you want to
-run on Spark, change `BeamExample` to `SparkExample` or `SparkDatasetExample`
+run on Spark, change `BeamExample` to `SparkExample` or `SparkDataFrameExample`
 and `--outputFilePath=output.txt` to `--outputFolder=output`):
 
 ```shell
@@ -159,7 +153,9 @@ output file paths.
 
 View the results.
 
-For Beam `cat output.txt` For Spark the output is written to a folder and the
+For Beam `cat output.txt`.
+
+For Spark the output is written to a folder and the
 result is stored in a file whose name starts with `part-00000`: `cat
 output/part-00000<...>`
 

java/BUILD

@@ -18,7 +18,7 @@ load("@bazel_common//tools/maven:pom_file.bzl", "pom_file")
 load("@rules_jvm_external//:defs.bzl", "java_export")
 
 # Update the following version for packaging of a new release.
-_DP_LIB_VERSION = "3.0.0"
+_DP_LIB_VERSION = "4.0.0"
 
 exports_files([
     "dp_java_deps.bzl",

pipelinedp4j/BUILD.bazel

@@ -30,7 +30,7 @@ kt_kotlinc_options(
 )
 
 # Update the following version for packaging of a new release.
-_RELEASE_VERSION = "0.0.1"
+_RELEASE_VERSION = "4.0.0"
 
 pom_file(
     name = "export_pom",

pipelinedp4j/README.md

@@ -18,10 +18,6 @@ You can use PipelineDP4j in Java, Kotlin or Scala.
 
 ## How to Use
 
-WARNING: Current API version (0.0.1) is experimental and will be changed in 2024
-without backward-compatibility. The experimental API won't be supported and
-maintained after that.
-
 ### Example
 
 <!-- TODO: create codelab and rewrite this section. -->
@@ -44,6 +40,3 @@ dependency from Maven repository. You can find it
 [here](https://mvnrepository.com/artifact/com.google.privacy.differentialprivacy.pipelinedp4j/pipelinedp4j).
 After adding this dependency into your project you can write the same code as in
 the example above and it will compile.
-
-Please, don't use `0.0.1` version in production code as it is experimental and
-its maintenance will be stopped in 2024 with release of the new version.